Temporal patterns and ensemble learning for environmental sound recognition
Bibliographic record
Abstract
This thesis explores features characterizing the temporal dynamics and the use of ensemble techniques to improve the performances of environmental sound recognition (ESR) system. Firstly, for acoustic scene classification (ASC), local binary pattern (LBP) technique is applied to extract the temporal evolution of Mel-frequency cepstral coefficients (MFCC) features, and the D3C ensemble classifier is adopted to optimize the system performance. The results show that the proposed method achieved a classification improvement of 8% compared to the baseline system. Secondly, a new approach for sound event detection (SED) using Nonnegative Matrix Factor 2- D Deconvolution (NMF2D) and RUSBoost techniques is presented. The idea is to capture the two dimensional joint spectral and temporal information from the time-frequency representation (TFR) while possibly separating the sound mixture into several sources. Besides, the RUSBoost ensemble technique is utilized in the event detection process to alleviate class imbalance in the training data. This method reduced the total error rate by 5% compared to the baseline method.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".